PhD Chapter 3

Results 2/3

This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.4.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Lin
## nugget = 0; sill = 0.00389; range = 2.29619; kappa = 0.5

City
## Model selected: Lin
## nugget = 0.00025; sill = 0.00602; range = 8.57222; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00021; sill = 0.02042; range = 4.52941; kappa = 0.5

Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.0072; range = 10.10924; kappa = 0.5

Sewers
## Model selected: Exp
## nugget = 0; sill = 0.03366; range = 43.15003; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06455; range = 4.27615; kappa = 0.5

Fisheries
## Model selected: Lin
## nugget = 0; sill = 0.02483; range = 3.40343; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.438 0.164 0.475 -0.435 -0.055 -0.693 -0.785 -0.741 -0.673 -0.627 -0.78 -0.76 -0.725 -0.737 0.315 0.002 -0.021 0.347 0.19
city -0.155 -0.067 0.427 -0.273 -0.096 -0.246 -0.163 -0.171 0.086 -0.004 -0.154 -0.243 -0.167 -0.015 -0.108 -0.036 -0.153 -0.055 0.035
dredging 0.275 -0.084 -0.091 0.103 0.055 0.264 0.19 0.407 0.574 0.649 0.55 0.219 0.324 0.482 -0.215 -0.133 0.049 -0.13 -0.023
industry 0.159 -0.071 -0.016 0.045 0.069 0.176 0.115 0.348 0.514 0.588 0.504 0.157 0.253 0.405 -0.246 -0.115 0.053 -0.198 -0.076
sewers 0.254 -0.037 -0.313 0.268 0.249 0.609 0.581 0.654 0.694 0.591 0.707 0.579 0.689 0.689 -0.353 -0.063 0.021 -0.369 -0.174
shipping 0.456 -0.249 -0.291 0.314 -0.015 0.537 0.504 0.618 0.693 0.677 0.708 0.549 0.576 0.687 -0.19 -0.06 0.022 -0.172 -0.095
fisheries -0.492 0.202 0.376 -0.378 -0.138 -0.567 -0.541 -0.552 -0.606 -0.576 -0.585 -0.54 -0.563 -0.613 0.309 0.173 -0.066 0.224 -0.015
cumulative_exposure 0.282 -0.114 -0.163 0.198 0.085 0.364 0.264 0.407 0.552 0.57 0.525 0.315 0.41 0.505 -0.108 -0.047 0.001 -0.125 -0.123
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 2.182e-06 0.08972 2.088e-07 2.619e-06 0.574 9.031e-17 9.296e-24 5.052e-20 1.466e-15 3.917e-13 2.796e-23 1.567e-21 7.356e-19 9.114e-20 0.0009079 0.9815 0.8288 0.0002382 0.04944
city 0.1087 0.4921 3.982e-06 0.004225 0.3206 0.01043 0.09275 0.07674 0.3781 0.964 0.1118 0.01126 0.08362 0.8744 0.2674 0.7138 0.1134 0.5708 0.7171
dredging 0.004038 0.3876 0.3492 0.2869 0.574 0.005687 0.04861 1.217e-05 8.309e-11 2.962e-14 6.813e-10 0.02309 0.000633 1.283e-07 0.02517 0.171 0.6121 0.1789 0.8096
industry 0.1007 0.465 0.8689 0.6459 0.4811 0.06919 0.2347 0.0002275 1.3e-08 2.144e-11 2.783e-08 0.1043 0.008203 1.389e-05 0.01022 0.236 0.5892 0.03971 0.4341
sewers 0.007974 0.702 0.000962 0.004998 0.009281 2.623e-12 4.439e-11 1.762e-14 8.768e-17 1.703e-11 1.192e-17 5.084e-11 1.659e-16 1.805e-16 0.000176 0.5189 0.8284 8.325e-05 0.07195
shipping 7.165e-07 0.009324 0.002213 0.0009345 0.8743 2.146e-09 2.655e-08 1.041e-12 1.003e-16 9.205e-16 1.105e-17 7.68e-10 7.258e-11 2.359e-16 0.04853 0.5351 0.8202 0.07554 0.3296
fisheries 6.275e-08 0.03585 6.17e-05 5.585e-05 0.1551 1.607e-10 1.476e-09 6.146e-10 3.727e-12 6.679e-11 2.989e-11 1.623e-09 2.366e-10 1.852e-12 0.001128 0.07296 0.496 0.01998 0.8735
cumulative_exposure 0.003134 0.2417 0.0927 0.04022 0.3831 0.0001071 0.005753 1.227e-05 5.814e-10 1.193e-10 5.558e-09 0.0008975 1.026e-05 2.504e-08 0.2678 0.6258 0.9893 0.1967 0.2062

3. Relationships with benthic communities

3.1. Taxa identity

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left panel) or biomass (right).

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 1.955, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida 15.2 28.1 32.3 32.8 12
Arthropoda 13.4 41.6 53.4 47.2 5
Cnidaria 0 0 0 0 0.25
Echinodermata 0.2 0.273 7.38 0.875 28.5
Mollusca 12 9.5 12 16.5 7.25
Nematoda 0 0.364 5 15.7 13.2
Nemertea 0 0.182 0 0.214 0
Sipuncula 0.4 0.455 0.429 0.107 0.5
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida 3.2 0.927 2.78 0.709 0.0953
Arthropoda 0.0221 0.0705 0.11 0.167 0.0344
Cnidaria 0 0 0 0 0.841
Echinodermata 0.00436 3.68 2.11 6.53 26.7
Mollusca 1.8 0.234 3.14 0.934 5.35
Nematoda 0 3.64e-05 0.000405 0.000678 0.000425
Nemertea 0 0.0777 0 3.93e-05 0
Sipuncula 0.0168 0.0191 0.00639 0.00803 0.00172

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 1.955, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

4. Regressions

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries
aquaculture 1 0.061 -0.364 -0.308 -0.672 -0.7 0.72
city 0.061 1 0.334 0.325 0.131 0.22 -0.201
dredging -0.364 0.334 1 0.961 0.668 0.686 -0.469
industry -0.308 0.325 0.961 1 0.691 0.598 -0.365
sewers -0.672 0.131 0.668 0.691 1 0.65 -0.581
shipping -0.7 0.22 0.686 0.598 0.65 1 -0.72
fisheries 0.72 -0.201 -0.469 -0.365 -0.581 -0.72 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown on the table below:

Predictor S N B H J
Depth + + +
Aquaculture
City
Dredging
Industry
Sewers -
Shipping
Fisheries
Adjusted \(R^{2}\) 0.2 0.02 0.02 0.3 0.15

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.2
Fitting linear model: S ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.755e-16 0.08611 -3.2e-15 1
depth 0.2244 0.1005 2.232 0.02785 *
aquaculture 0.1323 0.09663 1.369 0.174
city 0.0227 0.09686 0.2344 0.8152
dredging -0.03145 0.1112 -0.2828 0.7779
industry -0.1425 0.1338 -1.065 0.2895
sewers -0.1207 0.1376 -0.8777 0.3823
shipping 0.1112 0.09715 1.145 0.255
fisheries 0.1734 0.09747 1.779 0.07831 
## RMSE from cross-validation: 0.9206012
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.16 1.12 1.12 1.29 1.55 1.59 1.12 1.13

Density
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.866e-16 0.09512 1.961e-15 1
depth -0.2039 0.111 -1.837 0.06923
aquaculture -0.001951 0.1067 -0.01828 0.9855
city 0.07397 0.107 0.6913 0.491
dredging -0.08934 0.1228 -0.7273 0.4687
industry -0.1808 0.1478 -1.223 0.2242
sewers 0.1457 0.152 0.9588 0.34
shipping -0.07382 0.1073 -0.6879 0.4931
fisheries 0.08586 0.1077 0.7975 0.4271 
## RMSE from cross-validation: 1.045544
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.16 1.12 1.12 1.29 1.55 1.59 1.12 1.13

Biomass
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: B ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.226e-16 0.09534 -1.286e-15 1
depth -0.1858 0.1113 -1.67 0.09812
aquaculture -0.1569 0.107 -1.467 0.1456
city -0.1966 0.1072 -1.833 0.06984
dredging 0.008365 0.1231 0.06794 0.946
industry 0.1966 0.1482 1.327 0.1875
sewers -0.3756 0.1523 -2.466 0.01539 *
shipping -0.08828 0.1076 -0.8207 0.4138
fisheries -0.02274 0.1079 -0.2107 0.8335 
## RMSE from cross-validation: 1.007
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.16 1.12 1.12 1.29 1.55 1.59 1.12 1.13

Diversity
## FULL MODEL
## Adjusted R2 is: 0.3
Fitting linear model: H ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.142e-16 0.08045 3.906e-15 1
depth 0.4944 0.09391 5.265 8.175e-07 * * *
aquaculture 0.1016 0.09028 1.126 0.263
city 0.08284 0.0905 0.9154 0.3622
dredging 0.1236 0.1039 1.189 0.2372
industry -0.1761 0.125 -1.409 0.1621
sewers -0.095 0.1285 -0.7391 0.4616
shipping 0.02914 0.09077 0.321 0.7489
fisheries -0.03719 0.09107 -0.4084 0.6839 
## RMSE from cross-validation: 0.912593
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.16 1.12 1.12 1.29 1.55 1.59 1.12 1.13

Evenness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.392e-17 0.08846 1.573e-16 1
depth 0.4276 0.1032 4.141 7.277e-05 * * *
aquaculture 0.01141 0.09926 0.115 0.9087
city 0.09542 0.0995 0.959 0.3399
dredging 0.1729 0.1142 1.514 0.1333
industry -0.1712 0.1375 -1.246 0.2159
sewers -0.02464 0.1413 -0.1744 0.8619
shipping -0.07699 0.0998 -0.7715 0.4423
fisheries -0.1746 0.1001 -1.744 0.08432 
## RMSE from cross-validation: 1.066839
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.16 1.12 1.12 1.29 1.55 1.59 1.12 1.13

Annelida density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.14
Fitting generalized (poisson/log) linear model: annelids ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.314 0.01923 172.4 0 * * *
depth -0.3534 0.02507 -14.1 4.064e-45 * * *
aquaculture 0.117 0.01655 7.067 1.58e-12 * * *
city 0.09321 0.01825 5.108 3.248e-07 * * *
dredging -0.1001 0.02901 -3.448 0.0005642 * * *
industry -0.1914 0.03421 -5.595 2.203e-08 * * *
sewers -0.004193 0.03273 -0.1281 0.8981
shipping 0.1182 0.01862 6.348 2.177e-10 * * *
fisheries -0.07557 0.02359 -3.204 0.001354 * * 
## Unbiased RMSE from cross-validation: 35.86371
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.24 1.16 1.14 1.32 1.56 1.63 1.15 1.13

Arthropoda density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: arthropods ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.614 0.01706 211.8 0 * * *
depth -0.08957 0.01865 -4.802 1.568e-06 * * *
aquaculture -0.08194 0.02247 -3.646 0.0002666 * * *
city 0.1337 0.01535 8.708 3.081e-18 * * *
dredging -0.1031 0.02307 -4.468 7.88e-06 * * *
industry -0.696 0.03437 -20.25 3.528e-91 * * *
sewers 0.7409 0.0275 26.94 7.506e-160 * * *
shipping -0.07169 0.01609 -4.455 8.371e-06 * * *
fisheries 0.07277 0.01654 4.4 1.085e-05 * * * 
## Unbiased RMSE from cross-validation: 89.93936
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.26 1.1 1.11 1.23 1.98 2.08 1.11 1.13

Mollusca density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.18
Fitting generalized (poisson/log) linear model: molluscs ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.468 0.03023 81.64 0 * * *
depth 0.036 0.03013 1.195 0.2321
aquaculture 0.0685 0.02369 2.892 0.00383 * *
city 0.2253 0.02329 9.673 3.913e-22 * * *
dredging -0.08718 0.04091 -2.131 0.03308 *
industry 0.2496 0.0353 7.073 1.52e-12 * * *
sewers -0.3279 0.04496 -7.293 3.033e-13 * * *
shipping -0.2978 0.04308 -6.913 4.747e-12 * * *
fisheries 0.07146 0.02425 2.946 0.003215 * * 
## Unbiased RMSE from cross-validation: 20.60514
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.2 1.14 1.2 1.5 1.51 1.43 1.19 1.1

4.3. Multivariate regression

The model selected by the DistLM procedure has a \(R^{2}\) of 0.22. Colours represent the value of the cumulative exposure index (the bluer, the higher).

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